Abstract
Caribbean Small Island Developing States (SIDS) are highly vulnerable to extreme weather events and climate change. Caribbean SIDS climate vulnerability is worsened by their high level of financial exclusion. Many people do not have bank accounts and access to electronic fund transfer (EFT). As such, they cannot electronically receive funds before or after a natural disaster to cope with the effects. The financial exclusion problem can be addressed through a digital wallet. A digital wallet is a financial transaction application that securely stores a user’s banking and payment information on a cloud interface and allows the user to perform a transaction while hiding their banking information from a vendor. The biggest concern of users with regards to the use of digital wallets are its convenience and security. While digital wallets offer outstanding convenience of purchasing goods and services, data privacy and fraud risks deter people from adopting mobile payment. Potential fraud risk to digital wallets can be identified with anomaly detection techniques. The research problem investigated in this study is how the implementation of an artificial intelligence (AI) enhanced digital wallet can facilitate financial inclusion in the Caribbean, particularly in the context of disaster preparedness and recovery. Since security is an important aspect of a digital wallet, a sub-objective of this study is to derive an appropriate model for anomaly detection in financial transactions in a digital wallet. This study modifies Liu et al. [1] Gated Transformer Networks (GTN) architecture to allow for univariate time series classification. The corresponding new model is referred to as the Univariate Gated Transformer Network (UGTN). The UGTN is used for anomaly detection in financial data from a digital wallet. This study also provides policy recommendations for the implementation of a digital wallet to facilitate financial inclusion and climate resilience in Caribbean SIDS.
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